Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. We have also created a GitHub repository to index relevant papers on LLMs for recommendation, https://github.com/WLiK/LLM4Rec.

翻译：大型语言模型（LLMs）已成为自然语言处理（NLP）领域的强大工具，并近期在推荐系统（RS）领域受到广泛关注。这些模型通过自监督学习在海量数据上训练，在学习通用表征方面展现出卓越成效，并有望通过微调、提示调优等有效迁移技术增强推荐系统的多个环节。利用语言模型提升推荐质量的关键在于：借助其高质量的文本特征表征与广泛的外部知识覆盖能力，建立物品与用户之间的关联。为全面理解现有基于LLM的推荐系统，本综述提出了一种分类体系，将现有模型划分为两大范式：判别式LLM推荐（DLLM4Rec）与生成式LLM推荐（GLLM4Rec），其中后者首次得到系统梳理。在此基础上，我们系统性地回顾并分析了各范式下的现有推荐系统，深入探讨了其方法论、技术细节及性能表现。此外，我们识别出关键挑战并提出若干有价值的发现，以期为研究人员和实践者提供启发。我们还创建了GitHub仓库（https://github.com/WLiK/LLM4Rec）以索引LLM推荐相关论文。